Model-Based Strategies for Reducing the Complexity of Statistically Generated Test Suites

Autor:	Winfried Dulz
Rok vydání:	2013
Předmět:	Model-based testing Markov chain Computer science business.industry Process (engineering) Top-down and bottom-up design Machine learning computer.software_genre Reliability engineering Test (assessment) Test case Test suite Artificial intelligence business computer Selection (genetic algorithm)
Zdroj:	Lecture Notes in Business Information Processing ISBN: 9783642357015 SWQD
DOI:	10.1007/978-3-642-35702-2_7
Popis:	The main purpose of this paper is to show how model-based techniques are used to efficiently control the generation of less complex test suites. By directed adjusting specific probability values in the usage profile of a Markov chain usage model it is relatively easy to generate abstract test suites for different user classes and test purposes in an automated approach. A stepwise refinement process for hierarchical Markov chain usage models and choosing appropriate test generation, respectively selection strategies can reduce the complexity of the resulting test suite significantly. By using proper tools, like the TestUS Testplayer even less experienced test engineers will be able to efficiently generate abstract test cases and to graphically assess quality characteristics of different test suites.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::73ee921b136c6e93e6e735112a2b6e3d https://doi.org/10.1007/978-3-642-35702-2_7 Zobrazit plný text záznamu